DETAILED ACTION
Status of the Claims
The following is a non-final Office Action in response to claims filed 24 April 2024.
Claims 1-20 are pending.
Claims 1-20 have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices), and a manufacture (an article produced from raw or prepared materials by giving these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery). Thus, each of the claims falls within one of the four statutory categories (Step 1). However, the claim(s) recite(s) generating an optimal power management strategy based at least on the received historical power generation and costs thereof, and historical load information which is an abstract idea of a mental process as well as the abstract idea of performing computations in accordance with a mathematical formula on that data.
The limitations of “generating, by the computing system, an optimal power management strategy based at least on the historical power generation and cost information for the plurality of power stations and the historical load information for the consumer, wherein the optimal power management strategy includes at least one of a distribution strategy, a storage strategy, or a usage strategy; and causing, the consumer to manage power according to the optimal power management strategy,” as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process—concepts performed in the human mind (including an observation, evaluation, judgment, opinion) but for the recitation of generic computer components (Step 2A Prong 1). That is, other than reciting “A computer-implemented method of optimizing power management for a consumer, the method comprising:...a computing system,” in claim 1 or “A non-transitory computer-readable medium having computer-executable instructions stored thereon that, in response to execution by one or more processors of an energy control computing system, cause the energy control computing system to perform actions for optimizing power management for a consumer, the actions comprising:” in claim 12 nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a computer system,” or “energy control computing system” language, “generating” and “causing” in the context of this claim encompasses the user manually collecting, monitoring, and analyzing electrical power grid data which is a mental process similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, while some of the limitations may be based on mathematical concepts, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea (Step 2A, Prong One: YES).
This judicial exception is not integrated into a practical application (Step 2A Prong Two). Next, the claims only recite one additional element – using a computer system or an energy control computing system to perform the steps. The computer and processor in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of collecting information, analyzing it, and displaying certain results of the collection and analysis) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Specifically the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer or invoking computers as tools by adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d)(I) discussing MPEP 2106.05(f). Accordingly, the combination of these additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea, even when considered as a whole (Step 2A Prong Two: NO).
The claim does not include a combination of additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). As discussed above with respect to integration of the abstract idea into a practical application (Step 2A Prong 2), the combination of additional elements of using a computer system or an energy control computing system to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim. As such, the claim(s) is/are not patent eligible, even when considered as a whole (Step 2B: NO).
Claims 2-10 and 13-20 are dependent on claims 1 and 12 and include all the limitations of claims 1 and 12. Therefore, claims 2-10 and 13-20 recite the same abstract idea of “generating an optimal power management strategy based at least on the received historical power generation and costs thereof, and historical load information.” The claim(s) recite(s) the additional limitation(s) further limiting the data (energy sources/types, modeling parameters, optimization parameters) which is still directed towards the abstract idea previously identified and is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1 and 12, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 11 are dependent on claims 1 and 12 and include all the limitations of claim 1. Therefore, claim 11 recite the same abstract idea of “generating an optimal power management strategy based at least on the received historical power generation and costs thereof, and historical load information.” The claim(s) recite(s) the additional limitation(s) including a mixed integer linear programming technique which is a mathematical concept and is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1 and 12, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1-20 are therefore not eligible subject matter, even when considered as a whole.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-9 and 12-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shi (US PG Pub. 2020/0372588).
As per claims 1 and 12, Shi discloses a computer-implemented method of optimizing power management for a consumer, non-transitory computer-readable medium having computer-executable instructions stored thereon that, in response to execution by one or more processors of an energy control computing system, cause the energy control computing system to perform actions for optimizing power management for a consumer, the actions comprising:; the method comprising ():
receiving, by a computing system, historical power generation and cost information for a plurality of power stations (Still referring to FIG. 2, a goal of the proposed platform's user interface may be to leverage real-time data to help the user achieve the dual objectives of saving costs and reducing emissions. FIG. 3 shows an exemplary architecture 300 of an embodiment of a user interface. A portfolio map 304 may be used to view one or more properties if a user has multiple properties on system 100. A property view 308 may be used to illustrate data concerning any power generator, local grid, or other facilities and/or groups thereof a user may be responsible for managing. Each property view may include, without limitation a real time tab 312, which may be used to visualize real-time data, including without limitation real-time energy, carbon, grid, market, and/or weather data; an objective of real time tab may be to show the real-time status of the system, signals, and environment so that the impacts of costs and emissions can be evaluated. Real-time alerts may be triggered upon detection of forecasted significant increase in costs and/or emissions so that either autonomic or manual interventions may take place. Still referring to FIG. 3, each property view 308 may include, without limitation a historical tab 316, which may show historical data for user to analyze historical consumption, costs, and/or emissions, track the progress against goals, and/or project how metrics will look at a future time. Different ways of visualization, such as monthly, yearly, accumulated, and the like may help user better analyze historical performance and gain insights. Continuing to refer to FIG. 3, each property view 308 may include, without limitation a control tab 320. This tab may show a current and/or planned control strategy and its impact on costs and emissions. User may be able to compare different strategies and compare their performances using what-if scenarios and visualizations. An objective may be to help user evaluate different intervention strategies and visualize the impacts, Shi ¶37-¶39; power generator within the grid, ¶45; past costs, ¶84) (Examiner notes the power grid as including a plurality of power stations and the consumers thereof);
receiving, by the computing system, historical load information for the consumer (In historical visualization tab, emissions may be broken down into different fuel types, showing sources of consumed energy. Note that in market-based accounting, as described in further detail below, a breakdown may adapt to contracted power. In analytics tab, carbon may be shown in a form of a heat map to reveal its temporal patterns, Shi ¶41; consumer meter data, user’s historical metering, ¶35);
generating, by the computing system, an optimal power management strategy based at least on the historical power generation and cost information for the plurality of power stations and the historical load information for the consumer, wherein the optimal power management strategy includes at least one of a distribution strategy, a storage strategy, or a usage strategy (Embodiments disclosed herein incorporate new real-time carbon signals from the grid into the optimization of energy resources to minimize emission impacts while maximizing efficiency benefits. In embodiments, artificial intelligence and machine-learning methods are used to estimate real-time emission impacts based on grid data. Deep learning for real-time/online optimization and control may be used to minimize emission impacts while maximizing efficiency benefits under uncertainties of the ambient environment and user behaviors. A result may be a real-time data-driven software platform that implements methods and provides intuitive, compelling real-time visualizations, Shi ¶18; Still referring to FIG. 2, a goal of the proposed platform's user interface may be to leverage real-time data to help the user achieve the dual objectives of saving costs and reducing emissions, ¶37; This approach may harness mathematical models, such as machine-learning models, of real-time emissions and grid carbon intensities, which may be combined with prior knowledge for real-time optimization and control, ¶43); and
causing, by the computing system, the consumer to manage power according to the optimal power management strategy (Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like. All of outputs and results may be stored in an analytical data store 232, which may be implemented in any manner suitable for implementation of power quantities datastore 124 as described above and may be accessed via an interface 236 such as a user interface and/or API, Shi ¶36; In an embodiment, and still referring to FIG. 4, both real-time and forecasted signals may be sent to optimization engine. The outputs of optimization engine may include dispatches sent to control energy resources, ¶76).
As per claims 2 and 13, Shi discloses as shown above with respect to claims 1 and 12. Shi further discloses wherein generating the optimal power management strategy includes generating a power management strategy that achieves a goal of obtaining power from power stations having desired characteristics while minimizing cost (Still referring to FIG. 2, a goal of the proposed platform's user interface may be to leverage real-time data to help the user achieve the dual objectives of saving costs and reducing emissions, Shi ¶37; Still referring to FIG. 3, each property view 308 may include, without limitation a historical tab 316, which may show historical data for user to analyze historical consumption, costs, and/or emissions, track the progress against goals, and/or project how metrics will look at a future time. Different ways of visualization, such as monthly, yearly, accumulated, and the like may help user better analyze historical performance and gain insights, Continuing to refer to FIG. 3, each property view 308 may include, without limitation a control tab 320. This tab may show a current and/or planned control strategy and its impact on costs and emissions. User may be able to compare different strategies and compare their performances using what-if scenarios and visualizations. An objective may be to help user evaluate different intervention strategies and visualize the impacts, ¶38-¶39; In a practical example, a control problem as described above was simulated using 2017 real data to compare the results of three strategies: 1) minimization of demand charge only; 2) minimization of carbon emissions only; and 3) co-optimization of demand charge with carbon emissions. Results indicated that minimizing demand charge alone would lead to a cost savings of $102.53 thousand at an increased CO.sub.2 production of 2.37 tons, while carbon minimization only reduced carbon emissions by 14 tons at a 26.12-thousand-dollar price increase. Co-optimization was found to reduce costs by 100.12 thousand dollars while also reducing carbon emissions by 12.49 tons. This indicates an unexpected result, whereby both costs and carbon emissions may be reduced in amounts nearly equal to amounts saved by optimization of either variable alone. This demonstrates that optimization processes as disclosed herein may achieve near-optimal emission reductions with little impact on cost savings, producing a tenable carbon-reduction option where none previously existed, ¶82).
As per claims 3 and 14, Shi discloses as shown above with respect to claims 2 and 13. Shi further discloses wherein the cost of the power management strategy includes an exchange of renewable energy certificates (RECs) (Still referring to FIG. 4, computing device 104 may determine a current carbon emission rate as a function of the plurality of power output quantities. Current carbon emission rate may include, without limitation, a production-based current emission rate and/or a consumption-based current emission rate, as described above. This determination may be performed without limitation using location-based, market-based methods. A location-based method may consider average carbon intensity of power grids that provide electricity. A market-based method may consider contractual arrangements under which an organization procures power from specific sources, such as renewable energy. Both the location-based and market-based methods may be used for inventory accounting. Organizations may report their emissions using both methods. Still referring to FIG. 4, in location-based method, utilizing a real-time grid carbon intensity l.sub.a(t) defined as a real-time average carbon intensity of the grid at t, computing device 104 may calculate a user's real-time emissions due to the consumption of electricity consumed as: C.sub.l(t)=p(t)*l.sub.a(t)*?t, where p(t) is the user's net power demand (excluding onsite renewables) from a grid in kW and ?t is the time interval. Still referring to FIG. 4, in a market-based method, a user may purchase some of their power via power purchasing agreements (PPA) typically in the form of green energy such as solar and wind. Green power generated by contracted generation may be credited to user, for instance and without limitation in the form of renewable energy certificates (RECs). Let q.sub.i(t) be the power that is purchased from green contract i. If user's consumption p(t) is more than the total contracted power generation ?.sub.iq.sub.i(t), the residual p(t)??.sub.i(t) is the power from the grid. The user's real-time emission impact is then calculated based on the residual power from the grid, Shi ¶53-¶55).
As per claims 4 and 15, Shi discloses as shown above with respect to claims 2 and 13. Shi further discloses modeling cost of obtaining power from a first type of power station using a first technique; and modeling cost of obtaining power from a second type of power station using a second technique different from the first technique (any suitable cost model can be employed, Shi ¶79; minimize battery degradation cost, ¶80; There may be a cost associated with each energy resource's actions, ¶74) (Examiner notes that each energy resource having its own cost is the equivalent to a different cost modeling technique).
As per claims 5 and 16, Shi discloses as shown above with respect to claims 2 and 13. Shi further discloses wherein the power stations having desired characteristics are power stations that use carbon-free energy generation technologies (Still referring to FIG. 1, local grid monitoring device may be configured to report, using any suitable electronic communication protocol, a plurality of power output quantities of a plurality of power generators in a local grid corresponding to the local grid monitoring device. “Power generators,” as used in this disclosure, may include without limitation any kind of power plant or other device contributing to any power grid, including without limitation hydroelectric plants, coal power plants, oil power plants, natural gas power plants, photoelectric solar farms, solar collectors, wind farms, nuclear power plants, geothermal power plants, tidal power plants, or any other power plant and/or power production system that may occur to persons skilled in the art upon reviewing the entirety of this disclosure, Shi ¶22; purchase green energy, such as solar and wind, ¶55; based upon types of emissions, ¶43) (Examiner notes the green energy as the equivalent to the carbon-free energy generation techniques).
As per claims 6 and 17, Shi discloses as shown above with respect to claims 1 and 12. Shi further discloses wherein the optimal power management strategy includes the storage strategy and the distribution strategy; and wherein the method further comprises: managing charging and discharging of a local power storage system according to the storage strategy (Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging, diesel generation, fuel cells, flexible loads, and the like. All of outputs and results may be stored in an analytical data store 232, which may be implemented in any manner suitable for implementation of power quantities datastore 124 as described above and may be accessed via an interface 236 such as a user interface and/or API , Shi ¶36; market-based method including purchase of their power, ¶55; The outputs of optimization engine may include dispatches sent to control energy resources, ¶76)(Examiner notes the ability to control energy usage such as dispatching as the equivalent to a storage and distribution strategies).
As per claims 7 and 18, Shi discloses as shown above with respect to claims 1 and 12. Shi further discloses wherein the optimal power management strategy includes identities of power stations to be used on an hour-by-hour basis (Use of processes as described herein may permit provision of data with high temporal resolution, such as data provided in five-minute, 15-min, hourly, or monthly average temporal increments; temporal increments may be set to match with a desired granularity, such as a desired granularity of consumption data, Shi ¶26).
As per claims 8 and 19, Shi discloses as shown above with respect to claims 1 and 12. Shi further discloses further comprising predicting, by the computing system, future power generation and cost information for the plurality of power stations; and wherein the generation of the optimal power management strategy is further based on the predicted power generation and cost information (forecast, estimate and predict, Shi ¶34; Real-time alerts may be triggered upon detection of forecasted significant increase in costs and/or emissions so that either autonomic or manual interventions may take place, ¶37).
As per claims 9 and 20, Shi discloses as shown above with respect to claims 8 and 19. Shi further discloses wherein the prediction of future power generation and cost information is based at least in part on a weather prediction (At step 410, and still referring to FIG. 4, computing device 104 trains an emission projection machine-learning process. Training may include compiling a plurality of training data entries, each training data entry correlating a plurality of past power output quantities with at least a reported carbon emission datum, for instance as described above. Correlation may include direct correlation and/or computation of current carbon intensity for a set of power output quantities at a given time, for instance as described in further detail below, which may be correlated to emission data, as well as, without limitation, one or more additional and/or exogenous elements of data such as weather data, time of day, other circumstantial things, day of week, market, holiday, and/or season data, Shi ¶47; seasonal data, weather data from national weather service, ¶35) (Examiner notes the use of seasonal data as the weather prediction).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi (US PG Pub. 2020/0372588) and further in view of Jiang et al. (WO 2022/033069).
As per claim 10, Shi discloses as shown above with respect to claim 8. Shi does not expressly disclose wherein the predicted power generation and cost information includes information for at least one power station that has not yet been constructed.
However, Jiang teaches wherein the predicted power generation and cost information includes information for at least one power station that has not yet been constructed (predicted capacity, for an area, accurate load forecasting, Jiang Page 8 para. 1-Page 9 para. 2).
Both the Jiang and Shi references are analogous in that both are directed towards/concerned with energy grid planning and optimization. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Jiang’s ability to provide insights regarding building of a new power grid in Shi’s system to improve the system and method with reasonable expectation that this would result in an energy grid management system that is able to optimize a plurality of strategies.
The motivation being that regardless of the city’s positioning, in order to facilitate urban governance and achieve sustainable economic, humanistic, ecological and other sustainable development, the new belt-shaped area is subject to urban planning. During planning, various factors will be integrated into several functional areas. Since the new area is usually surrounded by a strip around the periphery of the old area, these functional areas are often arranged along the length of the strip. Different functions mean different load conditions. , which poses new challenges to smart power distribution, and it is necessary to provide a new strip-shaped power distribution planning method that meets the needs of smart city development planning (Jiang Page 3 para. 1).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi (US PG Pub. 2020/0372588) and further in view of Sarwat (US Patent No. 10, 326,280).
As per claim 11, Shi discloses as shown above with respect to claim 1. Shi does not expressly disclose wherein generating the optimal power management strategy includes solving an optimization problem using a mixed integer linear programming technique.
However, Sharwat teaches wherein generating the optimal power management strategy includes solving an optimization problem using a mixed integer linear programming technique (PDLB-IPM can be applied to relax a branch-and-cut mixed integer linear programming (BC-MILP) problem to accelerate its convergence. This method can generate a sequence of strictly positive primal and dual solutions to its problems and converges when it finds feasible primal and dual solutions which are complementary. A general primal dual LP problem can be written as follows, with its primal and dual components, Sharwat Col. 3 line 46-Col. 4 line 43).
Both the Sharwat and Shi references are analogous in that both are directed towards/concerned with energy grid optimization. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Sharwat’s ability to accelerate convergence of two liner programming problems in Shi’s system to improve the system and method with reasonable expectation that this would result in an energy grid management system that is able to optimize a plurality of strategies.
The motivation being that there is a need for a solution that synergistically integrates novel computational tools for smart RES generation forecasting and wide-area aggregation, optimization for providing dynamic RES hosting capacity, intelligent device synchronization, and on-demand ability to dispatch; complemented by state-of-the-art situationally aware visualization capable of providing in-depth operational visibility for real-time monitoring of the grid with complete accessibility to the entire grid (Sarwat Col. 1 lines 22-30).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional art can be located on the PTO-892):
Tarbell et al. (US PG Pub. 2011/0173110) Renewable energy system monitor.
Kelly et al. (US PG Pub. 2019/0296547) Monitoring electrical substation networks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW B WHITAKER whose telephone number is (571)270-7563. The examiner can normally be reached on M-F, 8am-5pm, EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629